Mass-ratio and complementarity effects simultaneously drive aboveground biomass in temperate Quercus forests through stand structure.

Forests play a key role in regulating the global carbon cycle, a substantial portion of which is stored in aboveground biomass (AGB). It is well understood that biodiversity can increase the biomass through complementarity and mass-ratio effects, and the contribution of environmental factors and stand structure attributes to AGB was also observed. However, the relative influence of these factors in determining the AGB of Quercus forests remains poorly understood. Using a large dataset retrieved from 523 permanent forest inventory plots across Northeast China, we examined the effects of integrated multiple tree species diversity components (i.e., species richness, functional, and phylogenetic diversity), functional traits composition, environmental factors (climate and soil), stand age, and structure attributes (stand density, tree size diversity) on AGB based on structural equation models. We found that species richness and phylogenetic diversity both were not correlated with AGB. However, functional diversity positively affected AGB via an indirect effect in line with the complementarity effect. Moreover, the community-weighted mean of specific leaf area and height increased AGB directly and indirectly, respectively; demonstrating the mass-ratio effect. Furthermore, stand age, density, and tree size diversity were more important modulators of AGB than biodiversity. Our study highlights that biodiversity-AGB interaction is dependent on the regulation of stand structure that can be even more important for maintaining high biomass than biodiversity in temperate Quercus forests.

Biodiversity increased both productivity and its spatial stability in temperate forests in northeastern China.

Although the relationship between biodiversity and ecosystem functioning has been extensively studied, it remains unclear if the relationships of biodiversity with productivity and its spatial stability vary along productivity gradients in natural ecosystems. Based on a large dataset from 2324 permanent forest inventory plots across northeastern China, we examined the intensity of species richness (SR) and tree size diversity (Hd) effects on aboveground wood productivity (AWP) and its spatial stability among different productivity levels. Structural equation modeling was applied, integrating abiotic (climate and soil) and biotic (stand density) factors. Our results demonstrated that both SR and Hd positively affected AWP and its spatial stability, and the intensity of these positive effects decreased with increasing productivity. At low productivity levels, SR and Hd increased spatial stability by reducing spatial variability and increasing mean AWP. At high productivity levels, stability increased only through mean AWP increase. Moreover, temperature and stand density affected the AWP directly and indirectly via biodiversity, and the strength and direction of these effects varied among different productivity levels. We concluded that biodiversity could simultaneously enhance productivity and its spatial stability in temperate forests, and that the effect intensity was uniform along productivity gradients, which provided a new perspective on relationships within biodiversity-ecosystem functioning.

[Difference analysis in estimating biomass conversion and expansion factors of masson pine in Fujian Province, China based on national forest inventory data: A comparison of three decision tree models of ensemble learning.] / åŸºäºŽæ¸ æŸ¥æ•°æ®çš„ç¦å»ºçœé©¬å°¾æ¾ç”Ÿç‰©é‡è½¬æ¢å’Œæ‰©å±•å› å­ä¼°ç®—å·®å¼‚è§£æž­­3ç§é›†æˆå­¦ä¹ å†³ç­–æ ‘æ¨¡åž‹çš„æ¯”è¾ƒ.

Biomass conversion and expansion factors (BCEFs) are important parameters for estimating carbon storage in forest biomass. Clarifying the source of differences in estimating BCEFs could reduce uncertainties in forest biomass carbon estimation. The decision tree models of ensemble learning can be used to properly figure out the source of differences in estimating BCEFs. However, the comparison of different decision tree models for analyzing differences in estimating BCEFs has never been reported. In this study, three models [the boosted regression trees (BRT), random forest(RF), and Cubist] and data of 331 masson pine plots from the 8th Chinese National Forest Inventory for Fujian Province were used to analyze the differences in estimating BCEFs (including above- and below-ground). The results showed that BCEFs were following right-skewed distribution, with the mean, minimum and maximum value being 0.69 t·m-3, 0.67 t·m-3 and 0.71 t·m-3, respectively. All three models performed well in BCEFs prediction and fitting, and could explain more than 92.8% variations of BCEFs. All three models showed that average DBH and volume were the top two highest relative importance predictors. BCEFs decreased with the increases of average DBH and volume. Stand characteristics factors, such as average DBH, volume, average age and average height, had great influence on BCEFs. Both soil factors and topographic factors had little influence on BCEFs. Using a few variables (such as average DBH, volume, average age and avera-ge height) which contained more BCEFs prediction information could have preferable forecasting precision when building BCEFs models. Moreover, widely representative samples with different average tree ages, average DBH and volume should be chosen to calculate BCEFs when applying constant BCEFs.

[Geostatistical analysis on the spatial pattern of Quercus mongolica population in different communities.] / ä¸åŒç¾¤è½è’™å¤æ Žç§ç¾¤ç©ºé—´æ ¼å±€çš„åœ°ç»Ÿè®¡å­¦åˆ†æž.

Taking Quercus mongolica population in the secondary forest of Q. mongolica as the research object, two plots in different stages of succession (A and B) were set up in Tazigou Forest Farm of Wangqing Forestry Bureau, Jilin Province. By applying the method of adjacent grid survey, each plot was divided into 100 units of 10 m×10 m and the spatial coordinates of each tree in the unit were accurately located to survey all the basic information of trees with diameter at breast height (DBH)≥1 cm. The degree, composition, scale and pattern of spatial heterogeneity of individual tree of Q. mongolica were analyzed by means of semi-variance function and fractal dimension of geostatistics. By using Kriging interpolation method, unbiased estimation of tree attribute with spatial autocorrelation was carried out, distribution map was drawn and spatial distribution pattern was analyzed. The results showed that the best semi-variance function of tree attributes in two plots was mainly distributed in an exponential model and a spherical model with an aggregated distribution. The degree of spatial autocorrelation and continuity of plot A were higher than that of plot B. The DBH and the east-west crown (CEW) had strong spatial heterogeneity and autocorrelation in the two plots. The tree attributes of both plots showed strong spatial heterogeneity in the north-south direction. In addition, there was strong spatial heterogeneity in the northwest-southeast direction of plot A and in the northeast-southwest of plot B. The strength of the spatial heterogeneity was higher and the scale being larger in plot A. The variations of DBH and CEW were obvious in plot A, while the variations of CEW and south-north crown (CSN) were obvious in plot B. The fractal dimension and semi-variogram function showed the same result. The tree attributes of plot A were mainly patchy and stripe, and the variation trend of spatial distribution pattern was obvious. The tree attributes of plot B was broken, with complex pattern. Those results indicated that the characteristics of population, community development, spatial scale and spatial horizontal direction might affect the spatial pattern of populations. The geostatistical analysis method is helpful to quantitatively and directly describe population growth and development trend, which can provide a theoretical basis for the sustainable management of Q. mongolica secondary forests in Northeast China.

Feature selection and classification of urinary mRNA microarray data by iterative random forest to diagnose renal fibrosis: a two-stage study.

Renal fibrosis is a common pathological pathway of progressive chronic kidney disease (CKD). However, kidney function parameters are suboptimal for detecting early fibrosis, and therefore, novel biomarkers are urgently needed. We designed a 2-stage study and constructed a targeted microarray to detect urinary mRNAs of CKD patients with renal biopsy and healthy participants. We analysed the microarray data by an iterative random forest method to select candidate biomarkers and produce a more accurate classifier of renal fibrosis. Seventy-six and 49 participants were enrolled into stage I and stage II studies, respectively. By the iterative random forest method, we identified a four-mRNA signature in urinary sediment, including TGFß1, MMP9, TIMP2, and vimentin, as important features of tubulointerstitial fibrosis (TIF). All four mRNAs significantly correlated with TIF scores and discriminated TIF with high sensitivity, which was further validated in the stage-II study. The combined classifiers showed excellent sensitivity and outperformed serum creatinine and estimated glomerular filtration rate measurements in diagnosing TIF. Another four mRNAs significantly correlated with glomerulosclerosis. These findings showed that urinary mRNAs can serve as sensitive biomarkers of renal fibrosis, and the random forest classifier containing urinary mRNAs showed favourable performance in diagnosing early renal fibrosis.

[Parameter sensitivity of simulating net primary productivity of Larix olgensis forest based on BIOME-BGC model].

Model based on vegetation ecophysiological process contains many parameters, and reasonable parameter values will greatly improve simulation ability. Sensitivity analysis, as an important method to screen out the sensitive parameters, can comprehensively analyze how model parameters affect the simulation results. In this paper, we conducted parameter sensitivity analysis of BIOME-BGC model with a case study of simulating net primary productivity (NPP) of Larix olgensis forest in Wangqing, Jilin Province. First, with the contrastive analysis between field measurement data and the simulation results, we tested the BIOME-BGC model' s capability of simulating the NPP of L. olgensis forest. Then, Morris and EFAST sensitivity methods were used to screen the sensitive parameters that had strong influence on NPP. On this basis, we also quantitatively estimated the sensitivity of the screened parameters, and calculated the global, the first-order and the second-order sensitivity indices. The results showed that the BIOME-BGC model could well simulate the NPP of L. olgensis forest in the sample plot. The Morris sensitivity method provided a reliable parameter sensitivity analysis result under the condition of a relatively small sample size. The EFAST sensitivity method could quantitatively measure the impact of simulation result of a single parameter as well as the interaction between the parameters in BIOME-BGC model. The influential sensitive parameters for L. olgensis forest NPP were new stem carbon to new leaf carbon allocation and leaf carbon to nitrogen ratio, the effect of their interaction was significantly greater than the other parameter' teraction effect.

A pilot trial assessing urinary gene expression profiling with an mRNA array for diabetic nephropathy.

BACKGROUND: The initiation and progression of diabetic nephropathy (DN) is complex. Quantification of mRNA expression in urinary sediment has emerged as a novel strategy for studying renal diseases. Considering the numerous molecules involved in DN development, a high-throughput platform with parallel detection of multiple mRNAs is needed. In this study, we constructed a self-assembling mRNA array to analyze urinary mRNAs in DN patients with aims to reveal its potential in searching novel biomarkers. METHODS: mRNA array containing 88 genes were fabricated and its performance was evaluated. A pilot study with 9 subjects including 6 DN patients and 3 normal controls were studied with the array. DN patients were assigned into two groups according to their estimate glomerular rate (eGFR): DNI group (eGFR>60 ml/min/1.73 m(2), n = 3) and DNII group (eGFR<60 ml/min/1.73 m(2), n = 3). Urinary cell pellet was collected from each study participant. Relative abundance of these target mRNAs from urinary pellet was quantified with the array. RESULTS: The array we fabricated displayed high sensitivity and specificity. Moreover, the Cts of Positive PCR Controls in our experiments were 24±0.5 which indicated high repeatability of the array. A total of 29 mRNAs were significantly increased in DN patients compared with controls (p<0.05). Among these genes, α-actinin4, CDH2, ACE, FAT1, synaptopodin, COL4α, twist, NOTCH3 mRNA expression were 15-fold higher than those in normal controls. In contrast, urinary TIMP-1 mRNA was significantly decreased in DN patients (p<0.05). It was shown that CTGF, MCP-1, PAI-1, ACE, CDH1, CDH2 mRNA varied significantly among the 3 study groups, and their mRNA levels increased with DN progression (p<0.05). CONCLUSION: Our pilot study demonstrated that mRNA array might serve as a high-throughput and sensitive tool for detecting mRNA expression in urinary sediment. Thus, this primary study indicated that mRNA array probably could be a useful tool for searching new biomarkers for DN.